melvinlee

---
name: kong-debug
description: Systematic debugging guide for Kong Gateway issues without relying on the Admin API. Use this skill whenever the user mentions Kong Gateway, Kong proxy, Kong plugins, 502/503/504 errors from Kong, Kong routes or services not working, Kong configuration issues, Konnect troubleshooting, or any API gateway problems that involve Kong. Trigger even if the user just says "my API gateway is broken" and Kong is in context. This variant focuses on proxy-side signals (response headers, logs, declarative config, container/network probes) for environments where the Admin API is disabled, locked down, or unreachable (e.g., Konnect data planes, hardened production).
---

# Kong Gateway Debugging (No Admin API)

Systematic playbook for diagnosing Kong Gateway issues using only proxy-side signals: response headers, logs, declarative config, container state, and network probes. This matches the reality of most production Kong deployments, where the Admin API is intentionally not exposed.

Mos

melvinlee

# LLM Wiki

A personal knowledge base maintained by Claude Code.
Based on Andrej Karpathy's LLM Wiki pattern.

## Purpose

This wiki is a structured, interlinked knowledge base for planning a trip to Japan.
Claude maintains the wiki. The human curates sources, asks questions, and guides the analysis.

melvinlee

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

melvinlee

Look it up yourself — 3 ways
1. crt.sh (Certificate Transparency logs)
https://crt.sh/?q=cust001.api.standardchartered.com
This will show all certs ever issued for that hostname, including issuer, validity dates, SANs, and CT log timestamps.
2. OpenSSL from your workstation
bashecho | openssl s_client \
  -connect <domain>:443 \
  -servername <domain> 2>/dev/null \
  | openssl x509 -noout -text
This gives you the full cert — subject, issuer, SANs, validity period, key algorithm, and signature.

melvinlee

if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser(description="Process Monitor MCP Server")
    parser.add_argument(
        "--transport",
        choices=["sse", "http"],
        default="http",
        help="Transport type: 'sse' or 'http' (streamable HTTP, default)",
    )

melvinlee

apiVersion: v1
kind: Pod
metadata:
  name: sqlplus-client
  labels:
    app: sqlplus
spec:
  containers:
  - name: sqlplus
    image: gvenzl/oracle-free:23

melvinlee

apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: oracle-db
  namespace: default
  labels:
    app: oracle-db
spec:
  serviceName: oracle-svc
  replicas: 1

melvinlee

#!/bin/bash
#
# EBS Volume Mount CLI Script
# This script attaches and mounts an EBS volume to an EC2 instance
#

set -e

# Define console colors
BLUE='\033[0;34m'

melvinlee

import streamlit as st 
import time 

col1, col2, col3 = st.columns([1,2,1])

col1.markdown("# Welcome to my app")

uploaded_photo = col2.file_uploader("Upload a photo")
camera_photo = col2.camera_input("Take a photo")

melvinlee

FROM mcr.microsoft.com/dotnet/core/sdk:3.1

# warmup NuGet package cache
WORKDIR /sln

COPY *.sln .
COPY projectfiles.tar . 
RUN tar -xvf projectfiles.tar
RUN dotnet restore \
    --source https://api.nuget.org/v3/index.json